---
title: "BC Socio-economic CEF indicators"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    source_code: embed
    css: custom.css
---

```{r setup, include=FALSE}
library(highcharter)
library(terra)
library(tidyterra)
library(sf)
library(DT)
library(tidyverse)
library(viridisLite)
library(forecast)
library(treemap)
library(arules)
library(geojsonsf)
library(flexdashboard)
thm <- 
  hc_theme(
    colors = c("#042f4b", "#005480", "#a4c1d8", "#85c495"),
    chart = list(
      backgroundColor = "transparent",
      style = list(fontFamily = "Source Sans Pro")
    ),
    xAxis = list(
      gridLineWidth = 1
    )
  )

```

# Page 1

## Inputs {.sidebar}

<div class="sidebar-content">
  <!-- Logo -->
  <img src="slogo.png" alt="Logo" class="sidebar-logo">
  
  <!-- Sidebar text -->
  <p>This is a draft dashboard showing the key CEF socio-economic indicators used in the current condition report.</p>

  <!-- Link to external resource -->
  <p>In this section we can link our website or the pdf of the report:</p>
  [CEF Economic Report](https://www2.gov.bc.ca/gov/content/environment/natural-resource-stewardship/cumulative-effects-framework)
</div>
## Column {.tabset data-width="500"}

### Cariboo

```{r}
data_2015 <- as.data.frame(read_csv("Cariboo_2015.csv"))
data_2020 <- as.data.frame(read_csv("Cariboo_2020.csv"))

# Create the interactive bar chart
highchart() %>%
  hc_chart(type = "column") %>%
  hc_title(text = "Interactive Bar Chart for 2015 and 2022 Data") %>%
  hc_xAxis(categories = data_2015$Sector) %>%
  hc_yAxis(title = list(text = "Jobs")) %>%
  hc_add_series(name = "Cariboo (2015)", data = data_2015$Jobs, visible = TRUE) %>%
  hc_add_series(name = "Cariboo (2020)", data = data_2020$Jobs) %>%
  hc_tooltip(valueSuffix = " jobs") %>%
  hc_exporting(enabled = TRUE) %>%
  hc_chart(events = list(
    load = JS("
      function() {
        var chart = this;
        chart.update({
          navigation: {
            menuItemStyle: {
              backgroundColor: '#f7f7f7',
              borderColor: '#ccc'
            },
            buttonStyle: {
              backgroundColor: '#2f7ed8',
              borderColor: '#2f7ed8'
            },
            menuItems: [
              {
                text: 'Show 2015 Data',
                onclick: function() {
                  chart.series[0].setVisible(true);
                  chart.series[1].setVisible(true);
                  chart.series[2].setVisible(true);
                  chart.series[3].setVisible(false);
                  chart.series[4].setVisible(false);
                  chart.series[5].setVisible(false);
                }
              },
              {
                text: 'Show 2022 Data',
                onclick: function() {
                  chart.series[0].setVisible(false);
                  chart.series[1].setVisible(false);
                  chart.series[2].setVisible(false);
                  chart.series[3].setVisible(true);
                  chart.series[4].setVisible(true);
                  chart.series[5].setVisible(true);
                }
              }
            ]
          }
        });
      }
    ")
  ))

```

### Peace River

```{r}
Peacedata_2015 <- as.data.frame(read_csv("Peace_2015.csv"))
Peacedata_2020 <- as.data.frame(read_csv("Peace_2020.csv"))

# Create the interactive bar chart
highchart() %>%
  hc_chart(type = "column") %>%
  hc_title(text = "Interactive Bar Chart for 2015 and 2022 Data") %>%
  hc_xAxis(categories = Peacedata_2015$Sector) %>%
  hc_yAxis(title = list(text = "Jobs")) %>%
  hc_add_series(name = "Peace River (2015)", data = Peacedata_2015$Jobs, visible = TRUE) %>%
  hc_add_series(name = "Peace River (2020)", data = Peacedata_2020$Jobs) %>%
  hc_tooltip(valueSuffix = " jobs") %>%
  hc_exporting(enabled = TRUE) %>%
  hc_chart(events = list(
    load = JS("
      function() {
        var chart = this;
        chart.update({
          navigation: {
            menuItemStyle: {
              backgroundColor: '#f7f7f7',
              borderColor: '#ccc'
            },
            buttonStyle: {
              backgroundColor: '#2f7ed8',
              borderColor: '#2f7ed8'
            },
            menuItems: [
              {
                text: 'Show 2015 Data',
                onclick: function() {
                  chart.series[0].setVisible(true);
                  chart.series[1].setVisible(true);
                  chart.series[2].setVisible(true);
                  chart.series[3].setVisible(false);
                  chart.series[4].setVisible(false);
                  chart.series[5].setVisible(false);
                }
              },
              {
                text: 'Show 2022 Data',
                onclick: function() {
                  chart.series[0].setVisible(false);
                  chart.series[1].setVisible(false);
                  chart.series[2].setVisible(false);
                  chart.series[3].setVisible(true);
                  chart.series[4].setVisible(true);
                  chart.series[5].setVisible(true);
                }
              }
            ]
          }
        });
      }
    ")
  ))

```

## Column {.tabset data-width="500"}

### Jobs by Region

```{r}

my_data_2020 <- vect("Map_Regeions.gpkg")
my_data_2020 <- as.data.frame(my_data_2020)

geojson2020 <- geojsonio::geojson_read("simple_regions_bc.geojson")


n <- 4
colstops <- data.frame(
  q = 0:n/n,
  c = substring(viridis(n + 1), 0, 7)) %>%
  list_parse2()

highchart() %>%
  hc_add_series_map(geojson2020, my_data_2020,
                    name = "Total Jobs",
                    value = "Total",
                    joinBy = c("CENSUS_DIVISION_NAME", "CENSUS_DIVISION_NAME"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.CENSUS_DIVISION_NAME}')) %>%
  hc_colorAxis(stops = colstops) %>%
  hc_legend(valueDecimals = 0) %>%
  hc_mapNavigation(enabled = TRUE)
```

### Test tab by Region

```{r}

my_data_2020 <- vect("Map_Regeions.gpkg")
my_data_2020 <- as.data.frame(my_data_2020)

geojson2020 <- geojsonio::geojson_read("simple_regions_bc.geojson")


n <- 4

colstops <- data.frame(
  q = 0:n/n,
  c = substring(rainbow(n + 1), 0, 7)) %>%
  list_parse2()
highchart() %>%
  hc_add_series_map(geojson2020, my_data_2020,
                    name = "Construction Jobs",
                    value = "Construction",
                    joinBy = c("CENSUS_DIVISION_NAME", "CENSUS_DIVISION_NAME"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.CENSUS_DIVISION_NAME}')) %>%
  hc_colorAxis(stops = colstops) %>%
  hc_legend(valueDecimals = 0) %>%
  hc_mapNavigation(enabled = TRUE)
```

# Table Data

## Column {data-width="500"}

### All data

```{r}
DT::datatable(my_data_2020, options = list(
  pageLength = 25
))
```